from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   42





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		@register_to_config
		def __init__(       self     :     str     ,  snake_case__     :     int = 3     ,  snake_case__     :     int = 3     ,  snake_case__     :     Tuple[str] = ("DownEncoderBlock2D",)     ,  snake_case__     :     Tuple[str] = ("UpDecoderBlock2D",)     ,  snake_case__     :     Tuple[int] = (6_4,)     ,  snake_case__     :     int = 1     ,  snake_case__     :     str = "silu"     ,  snake_case__     :     int = 3     ,  snake_case__     :     int = 3_2     ,  snake_case__     :     int = 2_5_6     ,  snake_case__     :     int = 3_2     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     float = 0.1_8215     ,  snake_case__     :     str = "group"     ,  )      ->	str:
				super().__init__()

				# pass init params to Encoder
				_lowerCamelCase				   =  Encoder(
				    in_channels=snake_case__     ,  out_channels=snake_case__     ,  down_block_types=snake_case__     ,  block_out_channels=snake_case__     ,  layers_per_block=snake_case__     ,  act_fn=snake_case__     ,  norm_num_groups=snake_case__     ,  double_z=snake_case__     ,  )

				_lowerCamelCase				   =  vq_embed_dim if vq_embed_dim is not None else latent_channels

				_lowerCamelCase				   =  nn.Convad(snake_case__     ,  snake_case__     ,  1		)
				_lowerCamelCase				   =  VectorQuantizer(snake_case__     ,  snake_case__     ,  beta=0.25     ,  remap=snake_case__     ,  sane_index_shape=snake_case__		)
				_lowerCamelCase				   =  nn.Convad(snake_case__     ,  snake_case__     ,  1		)

				# pass init params to Decoder
				_lowerCamelCase				   =  Decoder(
				    in_channels=snake_case__     ,  out_channels=snake_case__     ,  up_block_types=snake_case__     ,  block_out_channels=snake_case__     ,  layers_per_block=snake_case__     ,  act_fn=snake_case__     ,  norm_num_groups=snake_case__     ,  norm_type=snake_case__     ,  )
		@apply_forward_hook
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     torch.FloatTensor     ,  snake_case__     :     bool = True		)      ->	VQEncoderOutput:
				_lowerCamelCase				   =  self.encoder(snake_case__		)
				_lowerCamelCase				   =  self.quant_conv(snake_case__		)

				if not return_dict:
						return (h,)

				return VQEncoderOutput(latents=snake_case__		)
		@apply_forward_hook
		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     torch.FloatTensor     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = True		)      ->	Union[DecoderOutput, torch.FloatTensor]:
				# also go through quantization layer
				if not force_not_quantize:
						_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  self.quantize(snake_case__		)
				else:
						_lowerCamelCase				   =  h
				_lowerCamelCase				   =  self.post_quant_conv(snake_case__		)
				_lowerCamelCase				   =  self.decoder(snake_case__     ,  quant if self.config.norm_type == 'spatial' else None		)

				if not return_dict:
						return (dec,)

				return DecoderOutput(sample=snake_case__		)



		def   _snake_case       (       self     :     Optional[int]     ,  snake_case__     :     torch.FloatTensor     ,  snake_case__     :     bool = True		)      ->	Union[DecoderOutput, torch.FloatTensor]:
				_lowerCamelCase				   =  sample
				_lowerCamelCase				   =  self.encode(snake_case__		).latents
				_lowerCamelCase				   =  self.decode(snake_case__		).sample

				if not return_dict:
						return (dec,)

				return DecoderOutput(sample=snake_case__		)
from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   42
		lowerCAmelCase_     =   42





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     int     ,  snake_case__     :     int		)      ->	Any:
				_lowerCamelCase				   =  [[] for _ in range(snake_case__		)]
				_lowerCamelCase				   =  size
		def __getitem__(       self     :     Dict     ,  snake_case__     :     int		)      ->	Iterator[Edge]:
				return iter(self._graph[vertex]		)
		@property
		def   _snake_case       (       self     :     str		)      ->	int:
				return self._size
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Tuple:
				if weight not in (0, 1):
						raise ValueError('Edge weight must be either 0 or 1.'		)

				if to_vertex < 0 or to_vertex >= self.size:
						raise ValueError('Vertex indexes must be in [0; size).'		)

				self._graph[from_vertex].append(Edge(snake_case__     ,  snake_case__		)		)



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	int | None:
				_lowerCamelCase				   =  deque([start_vertex]		)
				_lowerCamelCase				   =  [None] * self.size
				_lowerCamelCase				   =  0

				while queue:
						_lowerCamelCase				   =  queue.popleft()
						_lowerCamelCase				   =  distances[current_vertex]
						if current_distance is None:
								continue

						for edge in self[current_vertex]:
								_lowerCamelCase				   =  current_distance + edge.weight
								_lowerCamelCase				   =  distances[edge.destination_vertex]
								if (
								    isinstance(snake_case__     ,  snake_case__		)
								    and new_distance >= dest_vertex_distance
								):
										continue
								_lowerCamelCase				   =  new_distance
								if edge.weight == 0:
										queue.appendleft(edge.destination_vertex		)
								else:
										queue.append(edge.destination_vertex		)

				if distances[finish_vertex] is None:
						raise ValueError('No path from start_vertex to finish_vertex.'		)

				return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()